If you are not aware of these reasons, this article will let you know everything about it apart from a brief description of CPU and GPU for your enhanced knowledge.
In This Article
- The GPUs usually come with a large number of cores in comparison to a CPU which makes them faster.
- The number of threads dedicated to a single process by a CPU is far too less than those dedicated by a GPU which makes them slower in operation.
- The CPU has to process a lot of different data and instructions and does it in a sequential manner which makes it slower than a GPU which typically helps in parallel processing of graphics only.
- GPUs typically use special algorithms and well structured threads to process images but CPUs have no specific order which makes them slower.
- The GPUs also come with a larger memory bandwidth and a faster cache memory, both of which make them faster than the CPUs.
8 Reasons Why the GPU Faster Than the CPU
GPUs are significantly faster than CPUs as they both follow instructions given to them in different ways.
The CPU focuses on sequential processing and therefore all its cores complete processing tasks one by one.
Whereas, the GPU was designed for multitasking without reduction in processing speed.
Even after hyperthreading CPUs are unable to match GPUs processing speeds, because GPUs do not have a limited number of cores.
Regardless of each individual core’s strength, when an entire graphics card is assigned a task of processing high-end graphics or other mathematical calculations, it effectively distributes its workload through parallel processing.
Therefore, GPU is used for offloading processor intensive tasks.
Other differentiating factors which make GPUs faster than CPUs are:
A CPUs cores are small but powerful. Whereas, GPUs have several thousands of cores even though they are even smaller and weaker in comparison.
However, it is this high number of cores that make GPUs much more powerful.
2. Number of Threads
A CPU is designed in such a way that each of its cores can be split into two visual threads, with each thread capable of functioning individually.
Whereas, a GPU is designed to function even with a single instruction and multiple threads functioning parallelly.
While CPUs have only one thread dedicated to an instruction, GPUs process much faster, as about 32 threads are dedicated to execute a single instruction.
3. Mode of Processing
CPU architecture is designed keeping in mind its main objective which is managing a computer with its limited resources.
It processes data in a sequential order, providing more resources to programs which require them to run properly.
A GPU’s main objective is not to support all processes but focus on processes which require use of its high-end resources.
It was designed, not as a replacement to CPU, but to reduce stress on it by facilitating parallel processing of instructions.
4. Implementation of Threads
GPUs have a more structured implementation of usage of threads when compared to CPUs.
This adds to GPU’s speed and power as CPUs use of threads for implementation of a task does not have any order to it.
Several algorithms for image processing can be implemented parallelly as GPUs have a proper system in place for rotation of threads for every instruction.
This method proves to be ideal as GPU resources, power and speed are used optimally.
Other ways in which GPUs are better than CPUs
5. Memory Bandwidth
Not only are GPUs faster than CPUs owing to their ability to parallelly process tasks, they also have much higher memory bandwidth.
A CPU and GPU manufactured around the same time differ in their performance ability, with GPU performing 10 times better in terms of memory system bandwidth.
Since, it is significantly higher than CPU’s performance.
Even with software applications that have not been fine-tuned to GPU’s configuration, it is possible to process data almost 100 times faster than CPUs.
This is possible as GPUs offer large caches for data storage which are able to keep up with their speed of processing data through multiple parallel processes.
6. Reduce Load
CPUs are used for optimally operating an entire computer; however, they are unable to reduce load on memory subsystems. GPUs are able to achieve this as the number of registers is dynamically changed.
It goes from less than a hundred to more than 250 as soon as it finds a GPU connected to its system.
7. Simultaneous Execution
A GPU’s ability to parallelly process data is well known, however, GPUs are also able to simultaneously execute completely different tasks, which are not interconnected in any way.
For example, while data is being copied to and from this device asynchronously, it can also decode video, process images for better graphics and perform calculations for neural networks.
None of these processes lag, as GPU provides adequate resources for each process individually, not letting them be affected by other processes being carried out simultaneously.
8. Shared Memory
Similar to CPUs, GPUs also have shared memory or cache.
However, in order to keep up with GPU’s processing speed, this shared memory is significantly faster than CPUs L1 cache.
Algorithms that require much more storage space locally due to their size or speed at which they need to be processed are stored in this shared memory space.
GPUs cannot be used to replace CPUs in a computer structure. Since, they do not have robust architecture like a CPU.
GPUs are currently only used for reducing load on CPUs as high-end resource intensive computing operations are shifted to GPUs for processing.
They are only used to make applications run faster and smoother from a user’s perspective.
Now that you know all the reasons, you no longer need to wonder why GPUs are faster than CPUs.